臺灣博碩士論文加值系統

趨勢預測在決策制定的領域扮演著重要的角色，而往往時間序列被最常被用來預測未來的趨勢。過去幾年來，有許多專家學者提出的模糊時間序列可解決傳統的時間序列問題，但仍存在著許多的限制和不足。很顯然地，在過去的研究裡頭，模糊關係之間的趨勢變化（往上趨勢、往下趨勢或持平趨勢）並沒有被納入考量，但往往它是左右我們預測結果重要的因素。
本研究認為必須將上述因素納入我們的預測過程。再者，過去傳統的模糊時間序列方法並沒有很合理的定義所要觀察的語意值，一般都是用直觀的方式切割語意的長度。除此之外，本研究更利用 微調因子讓我們的預測值更平滑且具有意義。因為在制定股市的買賣準則方面，每天的預測值必須要完全相異，否則便會失去預測的意義，而傳統的模糊時間序列最常令人詬病的就是切幾種語意就會產生幾種結果值。本研究進而提出權重趨勢的方法來突顯模糊關係之間的趨勢變化，過去在決定權重方面皆是以專家意見或者循序給定方法來完成。因此，將這些趨勢明確地區隔開可增進預測的效率。最後，我們採用台灣股市加權指數（TAIEX）以及阿拉巴馬入學人數為預測資料集並與其他方法做比較，實驗結果證明，本研究所提出的趨勢權重模糊時間序列在預測準確度上皆優於傳統的方法。

Trend forecasting plays an important role in decision making, and time series models are usually used to forecast future trend. Throughout the years, there have been various fuzzy methods proposed to solve traditional time series problems. Song and Chissom (1993a) defined fuzzy time series and proposed their methods to model fuzzy relationships among observations. On the other hand, different fuzzy time-series models have been proposed for various domain problems, such as enrollment forecasting, temperature forecasting the stock index forecasting, etc. It is obvious that trend of fuzzy relationships, which were currently ignored in previous studies, should be considered in forecasting. Further more; it is clear to see that, the major drawback of traditional methods is the lack of consideration in determining reasonable universe of discourse and the length of intervals. Moreover, we find that the neglect of information, which tells patterns of trend changes in the past history, should be considered in forecasting. Besides that, we should apply to smoothen our forecast value in the case of stock index prediction so that each day’s forecast value is clearly different, thus we can set some criteria in order to make trading possible.
In order to handle these kinds of problems, an objective and reasonable approach is then proposed. Traditionally, fuzzy relationships weights are either determined based on domain know-how, which could be elicited from domain experts or determined based on their chronological order. However each fuzzy relationship is likely to occur again once in a while, so it is critical to classify them into different trends in order to make more precise prediction. In this paper we propose a trend weighted fuzzy time series model to improve the forecast accuracy, and apply parameter to smoothen our forecast value, thus we can set some criteria in order to make trading possible. Using the Taiwan stock index and enrollment of Alabama as the forecasting data, the results show that our trend-based weighted fuzzy time series model outperforms other fuzzy time series models.

摘要
ABSTRACT
Content
List of figures
List of tables
1. Introduction
1.1 Background and Motivation
1.2 Research Objectives
1.3 Research Limitations
1.4 Organization of This Thesis
2. Preliminaries
2.1 Fuzzy logic
2.2 Fuzzy time series
3. Trend weighted fuzzy time series model
3.1 Research model and design
3.2 Proposed Algorithm
4. Verification and Comparision
4.1 TAIEX forecasting
4.2 Enrollment forecasting
5. Conclusion and Future work
Reference

[1]Chen, S.M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy sets and systems, Vol. 81, pp. 311-319.
[2]Faff, R.W, R.D. Brooks, Ho Yew Kee. (2002). New evidence on the impact of financial leverage on beta risk: A time-series approach. North American Journal of Economics and Finance, 13, pp. 1–20
[3]Huarng, K. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems, Vol. 123, pp. 387-394.
[4]Huarng, K. Heuristic models of fuzzy time series for forecasting. (2001). Fuzzy Sets and Systems, 123, pp. 137–154.
[5]Huarng, Kunhuang, Yu, Hui-Kuang (2005). A Type 2 fuzzy time series model for stock index forecasting. Physica A, 353, pp. 445–462
[6]Hussein Dourra, Pepe Siy. (2002). Investment using technical analysis and fuzzy logic. Fuzzy Sets and Systems, 127, pp. 221–240.
[7]Hwang, J.R, Chen, S.M, Lee, C.H. (1998). Handling forecasting problems using fuzzy time series. Fuzzy Sets Systems, 100, pp. 217–228.
[8]Miller, G.A. (1956). The magical number seven, plus or minus two: some limits on our capacity of processing information. The Psychological Review, Vol. 63, pp. 81-97.
[9]Ross, T.J. (2000). Fuzzy logic with engineering applications, International edition, McGraw-Hill, USA.
[10]Shin, H.W, Sohn, S.Y. (2004). Segmentation of stock trading customers according to potential value. Expert Systems with Applications, 27, pp. 27–33.
[11]Song, Q. and Chissom, B.S. (1993a). Fuzzy time series and its models. Fuzzy Sets and Systems, Vol. 54, pp. 269-277.
[12]Song, Q. and Chissom, B.S. (1993b). Forecasting enrollments with fuzzy time series – Part Ⅰ. Fuzzy sets and systems, Vol. 54, pp. 1-10.
[13]Song, Q. and Chissom, B.S. (1994). Forecasting enrollments with fuzzy time series – Part Ⅱ. Fuzzy sets and systems, Vol. 62, pp. 1-8.
[14]Tsaur, Ruey-Chyn, Jia-Chi O Yang and Hsiao-Fan Wang. (2005). Fuzzy Relation Analysis in Fuzzy Time Series Model. Computers and Mathematics with Applications, 49, pp. 539-548
[15]Wang, Y.F. (2002). Predicting stock price using fuzzy grey prediction system. Experts Systems with Applications, 22, pp. 33-39
[16]Yu, Hui-Kuang (2005). Weighted fuzzy time series models for TAIEX forecasting, Physica A, 349, pp. 609–624.
[17]Yu, Hui-Kuang (2005). A refined fuzzy time-series model for forecasting, Physica A, 346, pp. 657–681.
[18]Zadeh, L.A. (1965). Fuzzy Sets. Inform and Control, Vol. 8, pp. 338-353.
[19]Zadeh,L.A. (1975a). The concept of a linguistic variable and its application to approximate reasoning Ⅰ. Information Science, Vol. 8, pp. 199-249.
[20]Zadeh,L.A. (1975b). The concept of a linguistic variable and its application to approximate reasoning Ⅱ. Information Science, Vol. 8, pp. 301-357.
[21]Zadeh L.A. (1976). The concept of a linguistic variable and its application to approximate reasoning Ⅲ. Information Science, Vol. 9, pp. 43-80